Data Paper Zone II Versions EN3 Vol 4 (2) 2019
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Distributional data of shallow sea coral reef in Danzhou Bay Sanya Coral Reef National Nature Reserve from 1987 to 2018
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: 2018 - 11 - 20
: 2018 - 12 - 07
: 2019 - 05 - 08
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Abstract & Keywords
Abstract: ENVI 5.2 and ArcGIS 10.1 were used to process GF-2 image, Sentinel-2 image and Landsat TM/OLI image data covering Sanya Coral Reef National Nature Reserve. Assisted by field research data, we used visual interpretation and threshold segmentation to summarize the rules of coral reef distribution,and to extract the spatial distributional data of shallow sea coral reefs in the Danzhou Bay and Sanya Coral Reef National Nature Reserve. The accuracy of the results was verified by field spectrum information collected. Finally, we obtained the spatial distribution data of shallow sea coral reefs in phase 51. This data set can be used for the analysis of the temporal and spatial changes, eco-environmental changes of coral reefs, among others. With the support time series, it clearly reflects the distributional changes of coral reefs in Sanya National Nature Reserve.
Keywords: coral reef; remote sensing monitoring; visual interpretation; threshold segmentation; information extraction
Dataset Profile
TitleDistributional data of shallow sea coral Reef in Danzhou Bay and Sanya Coral Reef National Nature Reserve from 1987 to 2018
Data corresponding authorZhu Lanwei(zhulw@aircas.ac.cn)
Data authorsHuo Yanhui, Zhu Lanwei, Zhang Shaoyu, Yang Xu, Tang Shilin
Time range1987–2018
Geographical scopeThis data set has a geographic scope of 18°10'30"N - 18°15'30N", 109°20'50"E- 109°40'30"E and 19°33'00"N - 19°53'00N", 108°57'00"E- 109°16'00"E. Located in the south and the west coast of Hainan Province respectively.
Spatial resolution4 m, 10 m, 30 m
Data volume14.1 MB
Data format*.shp
Data service system<http://www.sciencedb.cn/dataSet/handle/687>
Sources of fundingMajor Science and Technology Program of Hainan Province (ZDKJ2016021); Genneral Programs of Natural Science Foundation of Hainan Province under Grant No.sy17zm01132; Open Fund of Guangdong Key Laboratory of Ocean Remote Sensing under Grant No. 2017B030301005-LORS1904; Major Program for Big Data Development of the National Development and Reform Commission (2016-999999-65-01-000696-01).
Dataset compositionThe data set is mainly including Hainan Danzhou Bay and Sanya Coral Reef National Nature Reserve, three different data sources of shallow sea coral reefs spatial data products, these data is saved as a compressed file (Data set of shallow coral reef distribution in Danzhou bay and Sanya coral reef national nature reserve between 1987 to 2018. Rar), each data exist alone named after years of space distribution of coral reef folder.
1.   Introduction
Coral reefs, known as "oases in the blue desert", are a very unique ecosystem in the ocean. Species living in coral reefs are extremely abundant[1]. Coral reef is a unique marine ecosystem with rich biodiversity, as well as an important fishing ground and marine tourism resources[2]. Coral reefs have high ecological and economic values. Coral reefs account for about 0.25% of the world's total ocean area, but they feed more than a quarter of the world's marine fish[3]. Coral reef ecosystem has amazing biodiversity and high primary productivity[4]. It is very sensitive to sea water temperature, acidity and pollutants. It can be used as a main biological indicator of marine environment. It can also play a role in regulating and optimizing the marine environment[5]. It is of great significance to timely grasp the spatial distribution of coral reefs for monitoring the marine ecological environment.
Based on Multi-temporal Remote Sensing data, this paper uses visual interpretation and threshold segmentation method to obtain the data products of coral reef spatial distribution in Danzhou Bay and Sanya National Nature Reserve from 1987 to 2018, and provides data sharing services. Relevant research results, as part of the coastal ecological environment monitoring in Hainan Province, reflect the spatial distributional characteristics of coral reefs under time series, and provide scientific data support for the protection and management of coral reefs in protected areas.
2.   Data and Method
2.1   Data
The data set is based on the GF-2 multispectral image of Sanya in Hainan Province with a resolution of 4 meters in 2017 provided by the Hainan High Fraction Data and Application Center, the 10-meter resolution Stinel-2 multispectral image of Hainan from 2016 to 2018 provided by the European Space Agency (http://scihub.copernicus.eu/) and the 30-meter resolution of Hainan from 1987 to 2018 provided by the United States Geological Survey (http://glovis.usgs.gov/). The Landsat TM/OLI multi-spectral remote sensing image is the data source. A total of 44 images with no or few clouds are obtained. Table 1 shows the serial number and imaging time information of the remote sensing satellite image used in this time.
Table 1   List of remote sensing image data
NumberDataSatelliteSensorImage Sequence Number
12017-02-16GF-2MSSGF2_PMS1_E109.5_N18.1_20170216_L1A0002189405
22017-02-16GF-2MSSGF2_PMS1_E109.6_N18.3_20170216_L1A0002189406
32017-04-11GF-2MSSGF2_PMS2_E109.5_N18.3_20170411_L1A0002299698
42016-12-09Sentinel-2MSIS2A_MSIL1C_20161209T031122_N0204_R075_T49QCA_20161209T032245.SAFE
52017-08-06Sentinel-2MSIS2A_MSIL1C_20170806T030541_N0205_R075_T49QCA_20170806T031514
62018-10-30Sentinel-2MSIS2A_MSIL1C_20181030T030831_N0206_R075_T49QCA_20181030T060940.SAFE
71987-02-05Landsat 5TMLT05_L1TP_124047_19870205_20170320_01_T1
81989-03-14Landsat 5TMLT05_L1TP_124047_19890314_20170204_01_T1
91991-11-15Landsat 5TMLT05_L1TP_124047_19911115_20170125_01_T1
101993-01-04Landsat 5TMLT05_L1TP_124047_19930104_20170121_01_T1
111995-01-10Landsat 5TMLT05_L1TP_124047_19950110_20170110_01_T1
121997-10-30Landsat 5TMLT05_L1TP_124047_19971030_20161229_01_T1
131999-12-23Landsat 5TMLT05_L1TP_124047_19991223_20161215_01_T1
142001-11-26Landsat 5TMLT05_L1TP_124047_20011126_20161210_01_T1
152003-02-01Landsat 5TMLT05_L1TP_124047_20030201_20161206_01_T1
162005-07-16Landsat 5TMLT05_L1TP_124047_20050716_20161125_01_T1
172007-01-27Landsat 5TMLT05_L1TP_124047_20070127_20161117_01_T1
182009-10-31Landsat 5TMLT05_L1TP_124047_20091031_20161018_01_T1
192011-02-07Landsat 5TMLT05_L1TP_124047_20110207_20161010_01_T1
202013-10-26Landsat 8OLILC08_L1TP_124047_20131026_20170429_01_T1
212014-11-14Landsat 8OLILC08_L1TP_124047_20141114_20170417_01_T1
222015-10-16Landsat 8OLILC08_L1TP_124047_20151016_20170403_01_T1
232016-02-05Landsat 8OLILC08_L1TP_124047_20160205_20170330_01_T1
242017-05-30Landsat 8OLILC08_L1TP_124047_20170530_20170615_01_T1
252018-02-10Landsat 8OLILC08_L1TP_124047_20180210_20180222_01_T1
261987-04-10Landsat 5TMLT05_L1TP_124046_19870410_20170320_01_T1
271989-07-20Landsat 5TMLT05_L1TP_124046_19890720_20170202_01_T1
281991-05-23Landsat 5TMLT05_L1TP_124046_19910523_20170126_01_T1
291993-05-12Landsat 5TMLT05_L1TP_124046_19930512_20170119_01_T1
301995-07-21Landsat 5TMLT05_L1TP_124046_19950721_20170108_01_T1
311997-06-08Landsat 5TMLT05_L1TP_124046_19970608_20161231_01_T1
321999-06-30Landsat 5TMLT05_L1TP_124046_19990630_20161219_01_T1
332001-08-06Landsat 5TMLT05_L1TP_124046_20010806_20161210_01_T1
342003-02-25Landsat 7ETMLE07_L1TP_124046_20030225_20170126_01_T1
352005-08-01Landsat 5TMLT05_L1TP_124046_20050801_20161124_01_T1
362007-07-06Landsat 5TMLT05_L1TP_124046_20070706_20161113_01_T1
372009-07-27Landsat 5TMLT05_L1TP_124046_20090727_20161023_01_T1
382011-07-09Landsat 7ETMLE07_L1TP_124046_20110709_20140705_01_T1
392013-05-19Landsat 8OLILC08_L1TP_124046_20130519_20170504_01_T1
402014-08-10Landsat 8OLILC08_L1TP_124046_20140810_20170420_01_T1
412015-06-26Landsat 8OLILC08_L1TP_124046_20150626_20170407_01_T1
422016-12-05Landsat 8OLILC08_L1TP_124046_20161205_20170317_01_T1
432017-09-19Landsat 8OLILC08_L1TP_124046_20170919_20170920_01_RT
442018-05-17Landsat 8OLILC08_L1TP_124046_20180517_20180517_01_RT
2.2   Method
2.2.1   Visual interpretation
Image interpretation refers to the basic process of obtaining information from images. Visual interpretation is a kind of remote sensing image interpretation. To put it another way, visual interpretation is the reverse process of remote sensing imaging. Visual interpretation is the process of recognizing targets from remote sensing images by means of image interpretation marks, extracting the information of distribution, structure and function of targets qualitatively and quantitatively, and expressing them on the geographic map[6].
Image interpretation marks, also known as interpretation elements, are image features that can directly reflect and discriminate the information of topographic features in remote sensing images, including shape, size, color and tone, shadow, position, structure, texture, pattern, layout and three-dimensional appearance, etc.[7].
Combining the characteristics of optical color, geographical location, shape and generating conditions of shore reefs on remote sensing images, a comprehensive and omni-directional interpretation mark is formed. With the help of remote sensing enhancement methods (such as image fusion, image stretching, principal component transformation, etc.), Landsat TM/OLI images are displayed in ArcGIS 10.1 software according to the optimal band combination and image enhancement mode, combined with field survey. Points to establish interpretation markers for shallow coral reefs[8] is shown in Table 2.
The visual interpretation data set acquires the spatial distribution information of shallow sea coral reefs according to the processing flow shown in Figure 1, loads the image in ENVI, first carries on the radiation calibration, transforms the DN value into the radiation brightness data, obtains the radiation brightness image; then carries on the FLAASH atmospheric correction, transforms the radiation brightness value into the surface reflectance, obtains the surface reflectance image; and then carries on the tailoring according to the protected area. Secondly, the normalized difference water body index (MNDWI) is calculated, the histogram threshold is segmented, and then the land-water mask is carried out to remove the land mask[9]. In ArcGIS 10.1, the images passing through the land mask are opened according to the combination of red, green and blue bands. Based on the visual interpretation signs determined in this section, the spatial distribution information of shallow sea coral reefs is obtained by digital vectorization method. Finally, the spatial distribution results of shallow-sea coral reefs in the 17-stage Danzhou Bay and the 19-stage Shallow-sea coral reefs in the Sanya Coral Reef National Nature Reserve were obtained.
Table 2   Interpretation Markers of Spatial Distribution Information of Shallow Sea Coral Reefs
Interpretation markNameImage characteristicsMark detail
LocationCrackCoastal sediment is too much to grow coral reefs. There is a channel gap between the reef and the coast with a depth of about 0.2-1.5 meters.
ColorBeachIn the RGB true color composite image, the beach color is blue, and the coral reef color is yellow-green. Note the distinction between beach and coral reef colors, which are easily confused.
ShapeStriped coral reefsCoastal reefs are reefs growing along the coast of continents or islands, generally showing strip or band distribution.
LocationRiver estuariesReef-building corals grow in seawater with salinity of 27 to 40 and the optimum salinity range is 34 to 36. Where freshwater enters the sea, the salinity will be diluted and reduced, which is not suitable for coral reef formation.
Depth of waterDepth of waterIt is generally believed that the range of water depth for reef-building corals is 0-50 m, and the optimum water depth is less than 20 m.

Fig. 1

Figure 1   Flow Chart of Visual Interpretation Technology
2.2.2 Automatic Extraction of Threshold Segmentation
Firstly, the original remote sensing image is radiometric calibrated, and DN value is converted into radiance data to obtain radiance image; after atmospheric correction of the calibrated remote sensing image, the radiance value is converted into surface reflectance, and the surface reflectance image is obtained; then, the coastline data and normalized difference water index (MNDWI) are used for threshold segmentation to mask land information. The spatial distribution information of coral reefs in shallow sea was obtained by histogram threshold segmentation using blue-green band ratio and normalized vegetation index (NDVI). The extraction process is shown in Figure 2.
The spatial distribution data of shallow sea coral reefs in Danzhou Bay and Sanya National Nature Reserve were obtained by threshold segmentation method (based on Landsat 8 images from 2013 to 2018).
Based on Sentinel-2 image from 2016 to 2018, the spatial distribution data of shallow sea coral reefs in Sanya Coral Reef National Nature Reserve were obtained by threshold segmentation method.

Fig. 2

Figure 2   Flow chart of threshold segmentation technology
3.   Data Sample Description
3.1   Data composition
This data set includes 51 periods of shallow sea coral reef spatial distribution data of Danzhou Bay and Sanya Coral Reef National Nature Reserve from 1987 to 2018. These data are stored in a compressed file ("Shallow sea coral reef distribution data set of Danzhou Bay and Sanya Coral Reef National Nature Reserve from 1987 to 2018.rar"). Each period of data is stored separately in a named folder for each year, totaling the total number. The volume is 14.1 MB. The corresponding SHP vector data files are stored in each folder. All data are in UTM-WGS84 coordinate system consistent with remote sensing images.
3.2   Data sample
Spatial distribution data of shallow coral reefs in Danzhou Bay are shown in Fig. 3-5. Spatial distribution data of shallow sea coral reefs in Sanya Coral Reef National Nature Reserve are shown in Fig. 6-11.

Fig. 3

Figure 3   Visual interpretation spatial distribution map of shallow coral reefs in Danzhou Bay

Fig. 4

Figure 4   Trend Map of Coral Reef in Shallow Sea of Danzhou Bay (1987-2018)

Fig. 5

Figure 5   Spatial distribution map of threshold segmentation of shallow coral reefs in Danzhou Bay (Landsat 8 RGB: 432, 2013, 2015 and 2018)

Fig. 6

Figure 6   Visual interpretation spatial distribution map of shallow sea coral reefs in Sanya Coral Reef Reserve (2017 GF-2 RGB:321)

Fig. 7

Figure 7   Visual Interpretation Spatial Distribution Map of Shallow Sea Coral Reefs in Sanya Coral Reef Reserve (Sentinel-2 RGB:432, 2018)

Fig. 8

Figure 8   Visual Interpretation Spatial Distribution Map of Shallow Sea Coral Reefs in Sanya Coral Reef Reserve (Landsat 8 RGB: 432, 2018)

Fig. 9

Figure 9   Trend map of visual interpretation spatial distribution of shallow sea coral reefs in Sanya Coral Reef Reserve (1987-2018)

Fig. 10

Figure 10   Spatial distribution map of shallow sea coral reef threshold segmentation in Sanya Coral Reef Reserve (Sentinel-2 RGB:432, 2018)

Fig. 11

Figure 11   Spatial distribution map of shallow sea coral reef threshold segmentation in Sanya Coral Reef Reserve (Landsat 8 RGB: 432, 2018)
4.   Data Quality Control and Evaluation
4.1   Accuracy Verification Method and Technical Route
The data set validates the accuracy by validating the fitting degree between the spectral reflectance curve of coral reefs on image and the measured spectral reflectance curve of coral reefs.
Firstly, spectral reflectance curves of coral reefs are extracted from images. Using ENVI software, the average spectral reflectance curve of coral reef patches was calculated and the average spectral reflectance was extracted.
Secondly, the spectral reflectance curves of coral reefs are acquired and generated by the portable ground object spectrometer (FieldSpec 3 produced by ASD Company, USA). The spectrum of coral reef was collected by underwater measurement method, and the spectrum was processed by viewspec Pro software. The spectral reflectance curve of coral reef was obtained[10]. The spectral reflectance values corresponding to the central wavelength of the image are extracted.
Finally, the corrcoef (x, y) function of MATLAB is used to calculate the correlation coefficient R between the average spectral reflectance of coral reefs and the spectral reflectance of corresponding wavelengths. The value of R in [-1,1], 0 means irrelevant; 0-1 means positive correlation, the larger the value, the more relevant; 1-0 means negative correlation, the smaller the value, the more relevant.

Fig. 12

Figure 12   Flow chart of accuracy verification technology
4.2   Accuracy verification results
Taking the Landsat 8 image of 2018 as an example, five patches (Xidaimao Island, Luhuitou Bay, Xiaodong Sea, Linqiangshi Island and Eman Port) with concentrated coral reefs were selected from the study area, and their average spectral reflectance was calculated. The calculated correlation coefficient R is shown in Table 3.
Table 3   Coefficient R
NameXidaimao IslandLuhuitou BayXiaodong SeaLinqiangshi IslandEman Port
Coefficient R0.87470.86340.86110.92710.9599



As can be seen from Table 3, the correlation coefficients R of the above five patches are all above 0.8, with the highest reliability of Eman Port being 96%, and the lowest reliability of Xiaodonghai being 86%. Overall, the reliability of coral reef extraction target can reach more than 80%.
5.   Data usage methods and suggestions
From 1987 to 2018, the spatial distribution data of shallow-sea coral reefs in Danzhou Bay and Sanya Coral Reef National Nature Reserve Phase 51 is SHP format. ArcGIS, ENVI and other related data processing software can be used to edit, query and follow-up analysis of the data set. This data set can provide basic data support for coral reef protection and regulation.
Acknowledgements
Thank the project team for the support and cooperation of relevant industry departments and units in Hainan Province in the field observation process, and express our heartfelt thanks. I would like to thank researchers Zhang Li and Liao Jingjuan for their methodological suggestions in validating the spatial distribution information of coral reefs. I would like to thank Guo Lianjie, Bi Jingpeng, Song Qixi, Zhang Yunfei and others for their work and contributions in image downloading, field investigation and technical route discussion.
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Data citation
Huo Yanhui, Zhu Lanwei, Zhang Shaoyu, Yang Xu, Tang Shilin. Distributional data of shallow sea coral reef in Danzhou Bay Sanya Coral Reef National Nature Reserve from 1987 to 2018 [DB/OL]. Science Data Bank, 2018. (2018-11-20). DOI: 10.11922/sciencedb.687.
Article and author information
How to cite this article
Huo Yanhui, Zhu Lanwei, Zhang Shaoyu, Yang Xu, Tang Shilin. Distributional data of shallow sea coral reef in Danzhou Bay Sanya Coral Reef National Nature Reserve from 1987 to 2018 [J/OL]. Chinese Scientific Data, 2018. (2018-12-07). DOI: 10.11922/csdata.2018.0080.zh.
Huo Yanhui
Major undertakings: Landsat TM/OLI image data acquisition, preprocessing, automatic extraction algorithm of coral reef spatial distribution information, etc.
(1994—),male,Xingtai City, Hebei Province,Master's degree,The research direction is remote sensing information analysis and environmental monitoring.
Zhu Lanwei
Major undertakings: The technical process design and quality control of the whole production of this data set, as well as accuracy verification and field investigation, etc.
zhulw@aircas.ac.cn
(1980—),female,Chifeng City, Inner Mongolia Autonomous Region,Associate Research Fellow,The research direction is remote sensing application and microwave remote sensing.
Zhang Shaoyu
Major undertakings: Accuracy validation and evaluation of coral reef spatial distribution data sets, field validation and investigation of coral reef spatial distribution, etc.
(1994—),male,Zhumadian City, Henan Province,Master's degree,The research directions are remote sensing information extraction, remote sensing application and natural heritage monitoring.
Yang Xu
Major undertakings: The design of precision verification scheme for coral reef spatial distribution data sets.
(1978—),male,Yingkou City, Liaoning Province,Research on Programming and Algorithms,The research direction is the application of remote sensing technology.
Tang Shilin
Major undertakings: Accuracy verification of coral reef spatial distribution data sets, field verification and investigation of coral reef spatial distribution, etc.
(1981—),male,Shaoyang City, Hunan Province,Ph.D. Postgraduates,The research direction is ocean remote sensing.
Major Science and Technology Program of Hainan Province (ZDKJ2016021); Genneral Programs of Natural Science Foundation of Hainan Province under Grant No.sy17zm01132; Open Fund of Guangdong Key Laboratory of Ocean Remote Sensing under Grant No. 2017B030301005-LORS1904; Major Program for Big Data Development of the National Development and Reform Commission (2016-999999-65-01-000696-01).
Publication records
Published: May 8, 2019 ( VersionsEN3
Released: Dec. 7, 2018 ( VersionsZH3
Published: May 8, 2019 ( VersionsZH5
References
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